Let us return to the question posed in the introduction on how
the data collected through the Malawian CEDRS on Lake Chilwa could be utilized
to gain information on the status of the fishery, and following the answer to
that summarize our conclusions.

5.1 On the data collected

The catch-rate data contain an enormous variability that, to a
large extent, we believe to be mostly administratively induced as caused by the
method of raising the daily catch and effort data to arrive at the estimates of
monthly catch and catch-rate. As a result, for instance, seasonality is hardly
detectable. Seasonality is expected to be clearly visible in the data in a
system with highly seasonal changes in productivity. Despite the high
variability, it is still possible to significantly detect trends in the various
catch-rates series within two to three years of monthly aggregated data. The
time windows, over which short- and long-term trends in total catch-rates could
be detected, are generally between two and three years of data, and occasionally
lower than that. Taking into account that the average duration between periods
of recession is six years, and that 30 percent of the variation in water levels
is accounted for by these cycles, this is rather a long time-frame to evaluate
the effects of any management measure that aims at improving catch-rates. The
speed of change in fish stocks in Lake Chilwa appears to be much faster than can
be detected through the present CEDRS.

5.2 On the effects of fluctuating water levels and
increased effort

The tremendous
increase in fishing effort is largely an increase in numbers of gear and labor
and not of changed technology. With highly contrasting lake levels, this makes
an analysis of the combined effects of effort and changing productivity as a
result of changing water levels possible.

The effect that changing water
levels have on stock levels is large: it can be detected despite the high
administratively induced background noise. Although the effect of
water levels seems to be immediate, only a small proportion of the annual
variation is explained by it. Two reasons can be given for this:

(1) The amount of error in the data
collection and subsequent handling obscures this effect. This error could be
considered as random noise during all the series, as sources of bias and error
are the same[20].

(2) The general trend of decreasing catch-rates is caused by
the tremendous increase in effort. Changes in water levels either obscure this
general trend if conditions are favorable as was the case between 1986 and 1991,
or effects of lowered levels in concentrating fish disguise the effects of
increased effort initially during receding water. Eventually the drops in
catch-rates speed up during the continued decrease in water levels.

Changing water levels are
reflected in an immediate effect on catch-rates in the various gears
employed, which means that the effect is on the stock abundance. The time lag in
the correlation between water levels and catch-rates is generally short (0-1
year) and long-term effects caused by strong or weak cohorts (year classes) of
fish over several years, are not detected - except possibly with Clarias.
Since most of the variation is accounted for within the first year
this indicates that the fishing pattern is aimed at small short-lived, or young
fish. However, despite the high effort, this fishing pattern does not seem to
influence the regenerative capacity of the stock, as this seems to be more a
function of water levels. In other words, when the environmental conditions are
favorable (strong water influx) the recruitment of new fish could be independent
of the fishing pressure, at least within the present range of observations. That
the recruitment appears much more dependent on favorable environmental
conditions, than on the actual parent stock sizes, is also manifested by the
rapid rebuilding of the stocks that is observed after each major lake level
recession. The Lake Chilwa fish stocks appear to be adapted to withstand high
natural depletions, and are therefore also able to sustain high exploitation
rates.

Delayed effects on catch-rates
through dilution and concentration of fish as a result
of changes in volumes of water and behavioral change in fish movements, result
in changing efficiency of gears. Both effects may be typical for the situation
in Chilwa and caused by both the small mesh sizes of the gears employed and the
areas fished. Much of the fish caught are small sized (0+ or 1 year old), and an
important part of the effort is employed along the shore or in reed beds. The
maximum size of Barbus paludinosus is only 12 cm while Oreochromis
shiranus (maximum size 25 cm) reaches maturity already at 12-15 cm (Furse,
1979). The fishery thus is adapted to catching small sizes. This means that as
the fishery maximizes on harvesting the production of juveniles or small species
before they are subjected to high natural mortalities, yields will also be
highly variable due to changing water levels. The amount of variation explained
at the aggregated level of years confirms this.

What does this mean for using the information gathered through
the CEDRS? Our analysis has concentrated mainly on total catch-rates by gear,
with an occasional excursion to individual species (groups). Long-term trends in
total catch-rates for all gears all point in the same downward direction. As
variation in aggregated total catch-rates by gear is lower than for the
individual species, it will be more difficult to detect both long and short-term
trends by species (groups), even on the aggregated level of the whole lake.
Using the information gathered at lower levels of aggregation - for example at
the level of main strata representing ecological areas, at village/beach level
or at the level of individual fishermen or by species(group) - will be
non-informative within a small time window but may be informative in a large
time window. At present, different gears are generally targeting different
species(groups), which means that gear specific trends can serve as an indicator
for their respective targets species. As some gears - e.g. fish traps - are also
fairly habitat specific, such trends will also provide information on changes in
those habitats. In a first approximation, short-term trends by gear could be
related to existing knowledge - both local-knowledge and scientific knowledge
(e.g. Kalk, McLachlan and Howard-Williams, 1979) - of the effects of changing
water levels related to the species and area specificity of the gear.

The immediate effect of changing water levels can be
illustrated without resorting to sophisticated statistical methods. Peaks in
total catch-rates and catch-rates of O. shiranus, Barbus
paludinosus, Clarias gariepinus and Other species in
Matemba seines all coincide with the significant increase in water levels
in the same year (Figures 5 and 6). This would indicate that relative change in
water level is probably a better indicator for changes in catch-rates (» stocks), than absolute lake-levels, also indicated by
the fact that maximum water levels usually score better than mean or minimum
water levels. Also in Kariba annual change in lake levels, reflecting the amount
of new inundated land every year (= new nutrients) scored better than mean
annual lake levels (Kolding, 1994; Karenge and Kolding, 1995).

By eliminating much of the perceived administratively
induced variance the information contained in the data collected through
the Malawian CEDRS could be made much more sensitive over the short-term to
changes both in effort and in productivity. Then the present analysis could be
easily extended at a lower aggregated level (by area and by species-gear
combination). Furthermore, at a higher aggregated level, overall effects of
management measures could be detected more quickly, even with the observed high
variation in catch-rates caused by changing water levels. This could make the
CEDRS a much better instrument to evaluate the biological effects of (co-)
management measures in such an adaptive environment.

[20] That the effect of water
levels on catch-rates can still be seen indicates that the assumption that
"administratively induced error" is a random effect may be correct.